Through a variety of transport modes, e.g.,

ride-sourcing public transit private car

Transport modal disparities

• Carbon intensity

• Spatiotemporal distributions of travel time and trips

• Transportation presents a major challenge to curbing climate change.

• Better informed policymaking requires up-to-date empirical data with good quality, low cost, and easy access.

• Emerging data sources enable deep and new insights from large-scale collection of human movement and transport systems.

Fig. 3 Tweets and road networks (car + public transit) in Stockholm region

1. What are the potentials and limitations of using emerging data sources for modelling mobility?

2. How can new data sources be properly modelled for characterising transport modal disparities?

RQ	#	Scope	Paper title
1	I	Population heterogeneity	From individual to collective behaviours: exploring population heterogeneity of human mobility based on social media data
	II	Travel demand	Feasibility of estimating travel demand using geolocations of social media data
	III		A mobility model for synthetic travel demand from sparse individual traces
2	IV	Travel time	Disparities in travel times between car and transit: spatiotemporal patterns in cities
	V	Modal competition	Ride-sourcing compared to its public-transit alternative using big trip data

• Why Twitter?

  • Easy access, low cost, large spatial and population coverage.

• Limitations of geotagged tweets

  • Biased population: young, highly-educated, urban residents.

  • Sparse sampling of the actual mobility.

  • Behaviour bias of reporting geolocations.

Uncommon places and leisure activities >> regularly visited places, e.g., home and workplace.

Fig. 6 Behaviour bias.Data used in Paper I

The reliability of estimated commuting trips using geotagged tweets is low.Paper II

Fig. 7A Commuting matrices.

Fig. 7B Commuting trip distance distributions.

Fig. 8 Four types of travellers.Paper I

• Local vs. Global traveller visits

  • Local: nearby locations.

  • Global: more distant locations.

• Returner vs. Explorer explores around

  • Returner: one centralised location.

  • Explorer: decentralised locations that are distant from each other.

• Twitter data are more suitable for city level than national level.

• The main obstacle of using Twitter data at a large spatial scale is the sparsity.

Fig. 9 National level (left) vs. city level (right).Paper II

spatial scale sampling method sample size

• User-based data collection works better than area-based data collection:

  A much larger number of geotagged tweets, a more complete picture of travel demand.

A density-based approach A mobility model

• A density-based approach is proposed to increase sample size:

Fig. 10 Trip-based approach (left)Lee et al., 2019 vs. density-based approach (right).Paper II

A density-based approach A mobility model

• An individual-based mobility modelPaper III fills the gaps in sparse mobility data, particularly geolocations of social media data.

• The model is designed to correct behaviour bias and sparsity issue.

Input- sparse mobility traces that can not be directly converted to trips.

Output- synthesised mobility converted to daily trips.

A density-based approach A mobility model

• The model-synthesised results have good agreements with the other data sources.

• An application: characterising trip distance distributions (domestic) of global regions:

Fig. 11 Distributions of synthesised domestic trips.Paper III

Data fusion framework for travel time calculationPaper IV:

Distribution of geotagged tweets represents the dynamic attractiveness of locations in cities.

Fig. 12 Geotagged tweets as destinations by hour of day (Stockholm region).

Fig. 13 Travel time ratio by hour of day (Sydney).

Spatiotemporal dynamics of travel time ratio (R)Paper IV.

Fig. 14 Travel time ratio over 24 hours.

• Travel time by PT is around twice as high as by car.

• PT can compete with car use during peak rush hours in Stockholm and Amsterdam.

	Does ride-sourcing complement, or compete with, public transit?

• How large is the share of ride-sourcing trips that can be substituted by taking public transit, if you are willing to walk up to 800 m to access and leave the transit station during daytime?

• Ride-sourcing trips: transit-competing vs. non-transit-competing.

  • What trip attributes and built environment are linked to the competition?

  • What are the implications for policymaking?

• The transit-competing trips account for 48.2%.

Fig. 15 Hot spots of ride-sourcing trips.Paper V

• The non-transit-competing trips tend to have a more spread-out distribution of pick-up and drop-off hot spots, including the international airport.

For travel time calculation For open trip data analysis

• Collected from a large area and population but at a cost of rich detail.

• Raw data: trip ID, pick-up and drop-off locations, pick-up and drop-off times, and cost.

Fig. 16 Data enrichment for ride-sourcing trip data.Paper V

A glass-box model enhanced by machine learning techniques: additive impact of factors and factor interactions.

Fig. 17 Impact of travel time by ride-sourcing (left) and transfer (right).Paper V

• Competition is more likely to happen when the travel time by ride-sourcing < 15 min.

• Requiring multiple transfers is associated with the competition between.

Land-use clusters of the study area.

Fig. 18 Impact of land-use cluster and its interaction with transfer.Paper V

• Low density/diversity of land use -> a lower probability of competition.

• Multiple transfers + middle density/diversity of land use -> a higher probability of competition.

• ✈️ Improve PT services that provide access to the international airport (informed by ride-sourcing hot spots);

• 🚌 Expand PT networks guided by the transit-competing ride-sourcing trips featuring short travel time but a big gap between the two modes;

• 💰 Incentivise the ride-sourcing trips that fill the gaps in the PT services that take a long time or require lengthy walking and transfers connecting to suburban areas.

  …

• ⭕️ ❗️ Easy access, low cost, but with biased population, behaviour bias, and sparsity issue.

• 👤 At the individual level, fundamental patterns are preserved.

• 👥 At the population level:

  • a reasonably good source for the overall travel demand estimation but not commuting demand.

  • careful consideration on spatial scale, sampling method, and sample size.

• 🔧 Innovative approaches for correcting the biases and increasing available data.

• 📦 Importance of data fusion approaches, especially given more and more open but incomplete data.

• Geotagged tweets is a good source for time-varying attractiveness of urban locations.

• 🚌 🚗 Public transit is virtually always slower than car and ride-sourcing.

• 📍 For making public transit more competitive, spatiotemporal details add nuanced insights to identify gaps and opportunities.

• Extending the use of social media data for mobility modelling.

  • De-biasing the data source.

  • Long-distance travels.

  • Combining the textual information with the location part.

• 🌐 Generating global synthetic mobility data for improving travel demand projections.

  • Bigger picture: energy systems’ modelling for the transport sector.

• 🚙 & 🚊 🚲 🚕 🚌 🚋 Combining multi-modal trip data for reducing transport carbon emissions.

  • Occupancy, shareability, and electrification of new mobility services provided by transportation network companies (TNCs).

• 🕸 Introducing the perspective of networks.

  • The relationship between user/traveller friendship networks (abstract) and their mobility networks (spatial).

  • How social segregation and spatial interactions shape each other?